Llama3VisionTransform

class torchtune.models.llama3_2_vision.Llama3VisionTransform(path: str, *, tile_size: int, patch_size: int, max_num_tiles: int = 4, special_tokens: Optional[Dict[str, int]] = None, max_seq_len: Optional[int] = None, image_mean: Optional[Tuple[float, float, float]] = None, image_std: Optional[Tuple[float, float, float]] = None, prompt_template: Optional[PromptTemplate] = None)

This transform combines the transforms for the different modalities of Llama 3.2 Vision. It is made up of the following transforms: - torchtune.models.llama3.Llama3Tokenizer - torchtune.models.clip.CLIPImageTransform - torchtune.modules.transforms.VisionCrossAttentionMask

This transform can be used as a drop-in replacement for tokenizers in recipes and generation but handles additional transformations from the __call__ method.

Parameters:

• path (str) – Path to pretrained tiktoken tokenizer file.

• tile_size (int) – Size of the tiles to divide the image into.

• patch_size (int) – Size of the patches used in the CLIP vision tranformer model. This is used to calculate the number of image embeddings per image.

• max_num_tiles (int) – Only used if possible_resolutions is NOT given. Maximum number of tiles to break an image into. This will be used to generate possible_resolutions, e.g. [(224, 224), (224, 448), (448, 224)] if max_num_tiles = 2 and tile_size = 224. Default 4.

• special_tokens (Optional[Dict[str, int]]) – mapping containing special text tokens and their registered token IDs. If left as None, this will be set to the canonical Llama3 special tokens.

• max_seq_len (Optional[int]) – maximum sequence length for tokenizing a single list of messages, after which the input will be truncated. Default is None.

• image_mean (Optional[Tuple[float, float, float]]) – Mean values of each channel, used for normalization.

• image_std (Optional[Tuple[float, float, float]]) – Standard deviations for each channel, used for normalization.

• prompt_template (Optional[PromptTemplate]) –

  template used to format the messages based on their role. This is used to add structured text around the actual messages. The structured text is used in three scenarios:

  • Task-specific templates to gear models for a particular task that it will expect after training

  • Model-specific templates that are required whenever the model is prompted, such as the [INST] tags in Llama2 and in Mistral

  • Community standardized templates, such as ChatMLTemplate

  The extra text will still get tokenized as normal text, not as special tokens. Default is None.

Examples

>>> model_transform = Llama3VisionTransform("/path/to/tokenizer.model", tile_size=224, patch_size=14)
>>> transformed_data = model_transform({"messages": user_message, "images": [img1, img2]})
>>> print(transformed_data["tokens"])
[1, 31587, 29644, 102, 2]
>>> print(transformed_data["images"][0].shape)
torch.Size([4, 3, 224, 224])

decode(token_ids: List[int], truncate_at_eos: bool = True, skip_special_tokens: bool = True) → str

Decode a list of token ids into a string.

Parameters:

• token_ids (List[int]) – The list of token ids.

• truncate_at_eos (bool) – Whether to truncate the string at the end of sequence token. Default is True.

• skip_special_tokens (bool) – Whether to show or skip special tokens in the decoded string. Default is True.

Returns:

The decoded string.

Return type:

str

tokenize_message(message: Message, tokenize_header: bool = True, tokenize_end: bool = True) → List[int]

Tokenize a message into a list of token ids.

Parameters:

• message (Message) – The message to tokenize.

• tokenize_header (bool) – Whether to prepend a tokenized header to the message.

• tokenize_end (bool) – Whether to append eot or eom id at the end of the message.

Returns:

The list of token ids.

Return type:

List[int]

tokenize_messages(messages: List[Message], add_eos: bool = True) → Tuple[List[int], List[bool]]

Tokenize a list of messages into a list of token ids and masks.

Parameters:

• messages (List[Message]) – The list of messages to tokenize.

• add_eos (bool) – Wether to add the tokenizer’s eos_id. Default True.

Returns:

The list of token ids and the list of masks.

Return type:

Tuple[List[int], List[bool]]